{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:34:47Z","timestamp":1777498487113,"version":"3.51.4"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["289903"],"award-info":[{"award-number":["289903"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["295496"],"award-info":[{"award-number":["295496"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["313268"],"award-info":[{"award-number":["313268"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["299915"],"award-info":[{"award-number":["299915"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["311273"],"award-info":[{"award-number":["311273"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["313266"],"award-info":[{"award-number":["313266"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["295504"],"award-info":[{"award-number":["295504"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["310507"],"award-info":[{"award-number":["310507"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["313267"],"award-info":[{"award-number":["313267"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167\u2009995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Code is available at https:\/\/github.com\/aalto-ics-kepaco.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty277","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T19:32:51Z","timestamp":1523561571000},"page":"i509-i518","source":"Crossref","is-referenced-by-count":79,"title":["Learning with multiple pairwise kernels for drug bioactivity prediction"],"prefix":"10.1093","volume":"34","author":[{"given":"Anna","family":"Cichonska","sequence":"first","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"},{"name":"Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapio","family":"Pahikkala","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandor","family":"Szedmak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heli","family":"Julkunen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antti","family":"Airola","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Heinonen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tero","family":"Aittokallio","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"},{"name":"Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland"},{"name":"Department of Mathematics and Statistics, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juho","family":"Rousu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051605115169900_bty277-B1","first-page":"1","author":"Airola","year":"2017"},{"key":"2023051605115169900_bty277-B2","first-page":"10","article-title":"Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach","volume":"1","author":"Ali","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051605115169900_bty277-B3","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btw433","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume":"32","author":"Ammad-Ud-Din","year":"2016","journal-title":"Bioinformatics"},{"key":"2023051605115169900_bty277-B4","first-page":"820","article-title":"Computational models for predicting drug responses in cancer research","volume":"18","author":"Azuaje","year":"2017","journal-title":"Brief, Bioinform"},{"key":"2023051605115169900_bty277-B5","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023051605115169900_bty277-B6","doi-asserted-by":"crossref","first-page":"i28","DOI":"10.1093\/bioinformatics\/btw246","article-title":"Fast metabolite identification with input output kernel regression","volume":"32","author":"Brouard","year":"2016","journal-title":"Bioinformatics"},{"key":"2023051605115169900_bty277-B7","doi-asserted-by":"crossref","first-page":"e1002503.","DOI":"10.1371\/journal.pcbi.1002503","article-title":"Prediction of drug-target interactions and drug repositioning via network-based inference","volume":"8","author":"Cheng","year":"2012","journal-title":"PLoS Comput. Biol"},{"key":"2023051605115169900_bty277-B8","doi-asserted-by":"crossref","first-page":"e278","DOI":"10.1136\/amiajnl-2013-002512","article-title":"Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties","volume":"21","author":"Cheng","year":"2014","journal-title":"J. Am. Med. Inform. Assoc"},{"key":"2023051605115169900_bty277-B9","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1517\/17460441.2015.1096926","article-title":"Identification of drug candidates and repurposing opportunities through compound-target interaction networks","volume":"10","author":"Cichonska","year":"2015","journal-title":"Exp. Opin. Drug Discov"},{"key":"2023051605115169900_bty277-B10","doi-asserted-by":"crossref","first-page":"e1005678.","DOI":"10.1371\/journal.pcbi.1005678","article-title":"Computational-experimental approach to drug-target interaction mapping: a case study on kinase inhibitors","volume":"13","author":"Cichonska","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023051605115169900_bty277-B11","first-page":"795","article-title":"Algorithms for learning kernels based on centered alignment","volume":"13","author":"Cortes","year":"2012","journal-title":"J. Mach. Learn. Res"},{"key":"2023051605115169900_bty277-B12","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat. Biotechnol"},{"key":"2023051605115169900_bty277-B13","doi-asserted-by":"crossref","first-page":"13091.","DOI":"10.1038\/ncomms13091","article-title":"Multi-omic data integration enables discovery of hidden biological regularities","volume":"7","author":"Ebrahim","year":"2016","journal-title":"Nat. Commun"},{"key":"2023051605115169900_bty277-B14","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1038\/nrd.2016.74","article-title":"Key factors for successful data integration in biomarker research","volume":"15","author":"Elefsinioti","year":"2016","journal-title":"Nature Rev Drug Discov"},{"key":"2023051605115169900_bty277-B15","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-009-1740-8","volume-title":"Regularization of Inverse Problems. Vol. 375","author":"Engl","year":"1996"},{"key":"2023051605115169900_bty277-B16","doi-asserted-by":"crossref","first-page":"82.","DOI":"10.1186\/1471-2105-14-82","article-title":"Learning a peptide-protein binding affinity predictor with kernel ridge regression","volume":"14","author":"Gigu\u00e8re","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023051605115169900_bty277-B17","doi-asserted-by":"crossref","first-page":"857","DOI":"10.2307\/2528823","article-title":"A general coefficient of similarity and some of its properties","volume":"27","author":"Gower","year":"1971","journal-title":"Biometrics"},{"key":"2023051605115169900_bty277-B18","first-page":"16.","article-title":"Chemical informatics functionality in R","volume":"18, 1","author":"Guha","year":"2007","journal-title":"J. Stat. Soft"},{"key":"2023051605115169900_bty277-B19","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1021\/ci00028a014","article-title":"Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information","volume":"35","author":"Hall","year":"1995","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023051605115169900_bty277-B20","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1093\/bioinformatics\/btn479","article-title":"Chemical substructures that enrich for biological activity","volume":"24","author":"Klekota","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051605115169900_bty277-B21","doi-asserted-by":"crossref","first-page":"e0159302.","DOI":"10.1371\/journal.pone.0159302","article-title":"Machine learning of protein interactions in fungal secretory pathways","volume":"11","author":"Kludas","year":"2016","journal-title":"PLoS One"},{"key":"2023051605115169900_bty277-B22","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1021\/acs.jcim.5b00539","article-title":"Kernel target alignment parameter: a new modelability measure for regression tasks","volume":"56","author":"Marcou","year":"2016","journal-title":"J. Chem. Inf. Model"},{"key":"2023051605115169900_bty277-B23","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1021\/acs.jmedchem.6b01611","article-title":"Profiling prediction of kinase inhibitors: toward the virtual assay","volume":"60","author":"Merget","year":"2017","journal-title":"J. Med. Chem"},{"key":"2023051605115169900_bty277-B24","doi-asserted-by":"crossref","first-page":"46.","DOI":"10.1186\/s12859-016-0890-3","article-title":"A multiple kernel learning algorithm for drug-target interaction prediction","volume":"17","author":"Nascimento","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2023051605115169900_bty277-B25","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug-target interaction predictions","volume":"16","author":"Pahikkala","year":"2015","journal-title":"Brief. Bioinformatics"},{"key":"2023051605115169900_bty277-B26","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1021\/cn3000422","article-title":"Exploring chemical space for drug discovery using the chemical universe database","volume":"3","author":"Reymond","year":"2012","journal-title":"ACS Chem. Neurosci"},{"key":"2023051605115169900_bty277-B27","first-page":"515","author":"Saunders","year":"1998"},{"key":"2023051605115169900_bty277-B28","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel Methods for Pattern Analysis","author":"Shawe-Taylor","year":"2004"},{"key":"2023051605115169900_bty277-B29","doi-asserted-by":"crossref","first-page":"i157","DOI":"10.1093\/bioinformatics\/btu275","article-title":"Metabolite identification through multiple kernel learning on fragmentation trees","volume":"30","author":"Shen","year":"2014","journal-title":"Bioinformatics"},{"key":"2023051605115169900_bty277-B30","doi-asserted-by":"crossref","first-page":"D344","DOI":"10.1093\/nar\/gks1067","article-title":"New and continuing developments at PROSITE","volume":"41","author":"Sigrist","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023051605115169900_bty277-B31","doi-asserted-by":"crossref","first-page":"D994","DOI":"10.1093\/nar\/gkx911","article-title":"PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies","volume":"46","author":"Smirnov","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023051605115169900_bty277-B32","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","article-title":"Identification of common molecular subsequences","volume":"147","author":"Smith","year":"1981","journal-title":"J. Mol. Biol"},{"key":"2023051605115169900_bty277-B33","first-page":"1","article-title":"Kinomewide profiling prediction of small molecules","volume":"12","author":"Sorgenfrei","year":"2017","journal-title":"ChemMedChem"},{"key":"2023051605115169900_bty277-B34","doi-asserted-by":"crossref","first-page":"R37.","DOI":"10.1186\/gb-2014-15-2-r37","article-title":"The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts","volume":"15","author":"Wagner","year":"2014","journal-title":"Genome Biol"},{"key":"2023051605115169900_bty277-B35","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"Yang","year":"2012","journal-title":"Nucleic Acids Res"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/i509\/50315679\/bioinformatics_34_13_i509.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/i509\/50315679\/bioinformatics_34_13_i509.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T05:12:47Z","timestamp":1684213967000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/13\/i509\/5045738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,27]]},"references-count":35,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2018,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty277","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,7,1]]},"published":{"date-parts":[[2018,6,27]]}}}